Digital Twins Are Reinventing Innovation(sloanreview.mit.edu)
sloanreview.mit.edu
Digital Twins Are Reinventing Innovation
https://sloanreview.mit.edu/article/how-digital-twins-are-reinventing-innovation/
16 comments
"Reinventing Innovation" is an absurdly vacuous phrase.
We need to innovate on reinvention.
Disclosure: My company, Pathmind, works in a space adjacent to digital twins.
Digital twins are an interesting idea that, like "cognitive computing", is easily abused by marketing, and will probably rake in a lot of consulting fees for people like the authors of this piece (Accenture Research) and companies like IBM.
The essence of a digital twin is a simulation complex enough to be useful in making predictions in the real world. (That's the "twin" part.) Making complex simulations, as you might imagine, is difficult. It requires effort, deep domain knowledge (rare talent), good feedback mechanisms with the real situation in question, and some means of managing that complexity.
Digital twins do exist in deployment. What differentiates them from, say, any old machine-learning model you might use for predictions is that a "digital twin" is probably used for a more complex task than just classification. That is, it's probably used to direct the actions of a system. The words imply a larger solution.
So one thing you see is simulations that embed machine-learning models and predict what actions to take in a given state. Think of it like AlphaGo applied to business scenarios.
What are the pitfalls? Real-world data in these environments is non-stationary and messy, so signal may be low, or the ways you find signal might change over time.
To make the "digital twin" useful you are probably integrating with large software systems not entirely in your control, which may be hard to reason about (ERP systems like SAP).
The digital twin idea, insofar as it includes large parametric models that depend on algorithms like deep reinforcement learning, matters now, because those models are able to find structure in complexity, and make ever more accurate predictions about what to do. That is, we're able to identify optimal actions in more complex situations, with techniques more sophisticated than expert systems.
All that aside, this sort of thing is already getting deployed under the right circumstances, and you could argue that it is the future of a lot of business operations in supply chain and manufacturing.
Digital twins are an interesting idea that, like "cognitive computing", is easily abused by marketing, and will probably rake in a lot of consulting fees for people like the authors of this piece (Accenture Research) and companies like IBM.
The essence of a digital twin is a simulation complex enough to be useful in making predictions in the real world. (That's the "twin" part.) Making complex simulations, as you might imagine, is difficult. It requires effort, deep domain knowledge (rare talent), good feedback mechanisms with the real situation in question, and some means of managing that complexity.
Digital twins do exist in deployment. What differentiates them from, say, any old machine-learning model you might use for predictions is that a "digital twin" is probably used for a more complex task than just classification. That is, it's probably used to direct the actions of a system. The words imply a larger solution.
So one thing you see is simulations that embed machine-learning models and predict what actions to take in a given state. Think of it like AlphaGo applied to business scenarios.
What are the pitfalls? Real-world data in these environments is non-stationary and messy, so signal may be low, or the ways you find signal might change over time.
To make the "digital twin" useful you are probably integrating with large software systems not entirely in your control, which may be hard to reason about (ERP systems like SAP).
The digital twin idea, insofar as it includes large parametric models that depend on algorithms like deep reinforcement learning, matters now, because those models are able to find structure in complexity, and make ever more accurate predictions about what to do. That is, we're able to identify optimal actions in more complex situations, with techniques more sophisticated than expert systems.
All that aside, this sort of thing is already getting deployed under the right circumstances, and you could argue that it is the future of a lot of business operations in supply chain and manufacturing.
Very interesting. Do you have a sense of which businesses are working in digital twinning successfully?
Wind turbine operations for example. Digital twins are experimented for predicting the condition of the turbines.
We see it up and down the supply chain, from manufacturing to warehouses to transport nodes.
Cool! What companies do the twinning? Is it handled in-house by the companies applying it or are they partnering with specialists?
Companies like GE, SAP, Mathworks, Dassault, PTC and Siemens all have digital twin platforms used by major manufacturers. Initially there was a period (2003 -2013) where twin systems were built my specialist media developers using 3rd party authoring tools and integrating with different system simulation tools. While this still happens for specialist or niche projects (or sometimes on very large projects with specific deadlines that require outsourced help). Increasingly most manufacturers build digital twins directy from design assets (CAD, CAM, Systems simulations etc) as part of the in-house product development process using extensions to their existing design and simulation tooling.
@ptrott2017 - i'd love to speak with you about this. please ping me at chris at pathmind dot com if you're open to a conversation.
Both. Depends on the size and skills of the company. Like most semi-rare skills, simulation modeling is something that large companies both perform internally and ask external consultants to do with them.
I believe Digital Twin was crowned buzzword of the 2019 by Gartner
I thought the article would be about the Winklevoss brothers.
In the era of "all digital", one must be very careful to make the future anew, not a copy of the past.
Our models of system are only a pale reproduction of how those systems work, rendering designs that are tuned by those "evil twins" bound by our current understanding of the system which can be lacking sometimes.
Nevertheless, in engineering, notably aerospace for example, those digital twins are crucial to make better parts, as thrusters for example, do not allow for continuous measurements of temperature (everything burns there).
It rendered hardware system design far more agile.
Yet the concepts to build a novel hardware is not encoded in this simulation.
Another example of "future is past, repeated" is machine learning. Carrying encoded stereotypes in predictions.
As we try to model everything with some notable failures, such as economy, let's just be aware of the limitations of those models.
Our models of system are only a pale reproduction of how those systems work, rendering designs that are tuned by those "evil twins" bound by our current understanding of the system which can be lacking sometimes.
Nevertheless, in engineering, notably aerospace for example, those digital twins are crucial to make better parts, as thrusters for example, do not allow for continuous measurements of temperature (everything burns there).
It rendered hardware system design far more agile.
Yet the concepts to build a novel hardware is not encoded in this simulation.
Another example of "future is past, repeated" is machine learning. Carrying encoded stereotypes in predictions.
As we try to model everything with some notable failures, such as economy, let's just be aware of the limitations of those models.
Interesting that the authors don't mention the book Mirror Worlds (https://en.wikipedia.org/wiki/Mirror_world), but the last line of the article does make an allusion to it.
It's called a cookie.
https://black-mirror.fandom.com/wiki/Cookie
https://black-mirror.fandom.com/wiki/Cookie
Interesting take. I think it's hard for some companies to imagine how transformative the technology can be. This is a good starting point.